Kidney FACS Notebook
Enter the directory of the maca folder on your drive and the name of the tissue you want to analyze.
tissue_of_interest = "Kidney"Load the requisite packages and some additional helper functions.
library(here)
library(useful)
library(Seurat)
library(dplyr)
library(Matrix)
library(ontologyIndex)
cell_ontology = get_ontology('https://raw.githubusercontent.com/obophenotype/cell-ontology/master/cl-basic.obo', extract_tags='everything')
validate_cell_ontology = function(cell_ontology_class){
in_cell_ontology = sapply(cell_ontology_class, function(x) is.element(x, cell_ontology$name) || is.na(x))
if (!all(in_cell_ontology)) {
message = paste0('"', cell_ontology_class[!in_cell_ontology], '" is not in the cell ontology
')
stop(message)
}
}
convert_to_cell_ontology_id = function(cell_ontology_class){
return(sapply(cell_ontology_class, function(x) as.vector(cell_ontology$id[cell_ontology$name == x])[1]))
}
save_dir = here('00_data_ingest', 'tissue_robj')# read the metadata to get the plates we want
plate_metadata_filename = here('00_data_ingest', '00_facs_raw_data', 'metadata_FACS.csv')
plate_metadata <- read.csv(plate_metadata_filename, sep=",", header = TRUE)
colnames(plate_metadata)[1] <- "plate.barcode"
plate_metadataSubset the metadata on the tissue.
tissue_plates = filter(plate_metadata, tissue == tissue_of_interest)[,c('plate.barcode','tissue','subtissue','mouse.sex')]
tissue_platesLoad the read count data.
#Load the gene names and set the metadata columns by opening the first file
filename = here('00_data_ingest', '00_facs_raw_data', 'FACS', paste0(tissue_of_interest, '-counts.csv'))
raw.data = read.csv(filename, sep=",", row.names=1)
# raw.data = data.frame(row.names = rownames(raw.data))
corner(raw.data)Make a vector of plate barcodes for each cell
plate.barcodes = lapply(colnames(raw.data), function(x) strsplit(strsplit(x, "_")[[1]][1], '.', fixed=TRUE)[[1]][2])
head(plate.barcodes)[[1]]
[1] "MAA000545"
[[2]]
[1] "MAA000545"
[[3]]
[1] "MAA000545"
[[4]]
[1] "MAA000545"
[[5]]
[1] "MAA000545"
[[6]]
[1] "MAA000545"Use only the metadata rows corresponding to Bladder plates. Make a plate barcode dataframe to “expand” the per-plate metadata to be per-cell.
barcode.df = t.data.frame(as.data.frame(plate.barcodes))
rownames(barcode.df) = colnames(raw.data)
colnames(barcode.df) = c('plate.barcode')
head(barcode.df) plate.barcode
A14.MAA000545.3_8_M.1.1 "MAA000545"
E1.MAA000545.3_8_M.1.1 "MAA000545"
M4.MAA000545.3_8_M.1.1 "MAA000545"
O21.MAA000545.3_8_M.1.1 "MAA000545"
P4.MAA000545.3_8_M.1.1 "MAA000545"
A16.MAA000545.3_8_M.1.1 "MAA000545" rnames = row.names(barcode.df)
meta.data <- merge(barcode.df, plate_metadata, by='plate.barcode', sort = F)
row.names(meta.data) <- rnames
# Sort cells by plate barcode because that's how the data was originally
meta.data = meta.data[order(meta.data$plate.barcode), ]
corner(meta.data)raw.data = raw.data[, rownames(meta.data)]
corner(raw.data)Process the raw data and load it into the Seurat object.
# Find ERCC's, compute the percent ERCC, and drop them from the raw data.
erccs <- grep(pattern = "^ERCC-", x = rownames(x = raw.data), value = TRUE)
percent.ercc <- Matrix::colSums(raw.data[erccs, ])/Matrix::colSums(raw.data)
ercc.index <- grep(pattern = "^ERCC-", x = rownames(x = raw.data), value = FALSE)
raw.data <- raw.data[-ercc.index,]
# Create the Seurat object with all the data
tiss <- CreateSeuratObject(raw.data = raw.data, project = tissue_of_interest,
min.cells = 5, min.genes = 5)
tiss <- AddMetaData(object = tiss, meta.data)
tiss <- AddMetaData(object = tiss, percent.ercc, col.name = "percent.ercc")
# Change default name for sums of counts from nUMI to nReads
colnames(tiss@meta.data)[colnames(tiss@meta.data) == 'nUMI'] <- 'nReads'
# Create metadata columns for cell_ontology_classs and subcell_ontology_classs
tiss@meta.data[,'free_annotation'] <- NA
tiss@meta.data[,'cell_ontology_class'] <- NA
tiss@meta.data[,'subcell_ontology_class'] <- NACalculate percent ribosomal genes.
ribo.genes <- grep(pattern = "^Rp[sl][[:digit:]]", x = rownames(x = tiss@data), value = TRUE)
percent.ribo <- Matrix::colSums(tiss@raw.data[ribo.genes, ])/Matrix::colSums(tiss@raw.data)
tiss <- AddMetaData(object = tiss, metadata = percent.ribo, col.name = "percent.ribo")A sanity check: genes per cell vs reads per cell.
GenePlot(object = tiss, gene1 = "nReads", gene2 = "nGene", use.raw=T)
Filter out cells with few reads and few genes.
tiss <- FilterCells(object = tiss, subset.names = c("nGene", "nReads"),
low.thresholds = c(500, 50000), high.thresholds = c(25000, 2000000))Normalize the data, then regress out correlation with total reads
tiss <- NormalizeData(object = tiss, scale.factor = 1e6)Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|tiss <- ScaleData(object = tiss)[1] "Scaling data matrix"
|
| | 0%
|
|===========================================================================================================================| 100%tiss <- FindVariableGenes(object = tiss, do.plot = TRUE, x.low.cutoff = 0.7 , x.high.cutoff = Inf, y.cutoff = 0.4)Calculating gene means
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Run Principal Component Analysis.
tiss <- RunPCA(object = tiss, do.print = FALSE)
tiss <- ProjectPCA(object = tiss, do.print = FALSE)
Later on (in FindClusters and TSNE) you will pick a number of principal components to use. This has the effect of keeping the major directions of variation in the data and, ideally, supressing noise. There is no correct answer to the number to use, but a decent rule of thumb is to go until the plot plateaus.
PCElbowPlot(object = tiss)
Choose the number of principal components to use.
# Set number of principal components.
n.pcs = 8The clustering is performed based on a nearest neighbors graph. Cells that have similar expression will be joined together. The Louvain algorithm looks for groups of cells with high modularity–more connections within the group than between groups. The resolution parameter determines the scale…higher resolution will give more clusters, lower resolution will give fewer.
For the top-level clustering, aim to under-cluster instead of over-cluster. It will be easy to subset groups and further analyze them below.
# Set resolution
res.used <- 2.5
tiss <- FindClusters(object = tiss, reduction.type = "pca", dims.use = 1:n.pcs,
resolution = res.used, print.output = 0, save.SNN = TRUE)To visualize
# If cells are too spread out, you can raise the perplexity. If you have few cells, try a lower perplexity (but never less than 10).
tiss <- RunTSNE(object = tiss, dims.use = 1:n.pcs, seed.use = 10, perplexity=30)# note that you can set do.label=T to help label individual clusters
TSNEPlot(object = tiss, do.label = T)
Check expression of genes of interset.
Dotplots let you see the intensity of exppression and the fraction of cells expressing for each of your genes of interest.
How big are the clusters?
table(tiss@ident)
0 1 2 3 4 5 6 7 8 9
71 65 60 58 57 55 53 45 39 14 Which markers identify a specific cluster?
clust.markers <- FindMarkers(object = tiss, ident.1 = 7, ident.2 = c(2,3,4,6), only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 05sprint(x = head(x= clust.markers, n = 20, avg_logFC))You can also compute all markers for all clusters at once. This may take some time.
tiss.markers <- FindAllMarkers(object = tiss, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
| | 0 % ~calculating
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 49s
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 19s
| | 0 % ~calculating
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|++++++++++++++++++++ | 40% ~15s
|+++++++++++++++++++++ | 41% ~15s
|+++++++++++++++++++++ | 42% ~15s
|++++++++++++++++++++++ | 43% ~14s
|++++++++++++++++++++++ | 44% ~14s
|+++++++++++++++++++++++ | 45% ~14s
|+++++++++++++++++++++++ | 46% ~14s
|++++++++++++++++++++++++ | 47% ~13s
|++++++++++++++++++++++++ | 48% ~13s
|+++++++++++++++++++++++++ | 49% ~13s
|+++++++++++++++++++++++++ | 50% ~13s
|++++++++++++++++++++++++++ | 51% ~12s
|+++++++++++++++++++++++++++ | 52% ~12s
|+++++++++++++++++++++++++++ | 53% ~12s
|++++++++++++++++++++++++++++ | 54% ~11s
|++++++++++++++++++++++++++++ | 55% ~11s
|+++++++++++++++++++++++++++++ | 56% ~11s
|+++++++++++++++++++++++++++++ | 57% ~11s
|++++++++++++++++++++++++++++++ | 58% ~10s
|++++++++++++++++++++++++++++++ | 59% ~10s
|+++++++++++++++++++++++++++++++ | 60% ~10s
|+++++++++++++++++++++++++++++++ | 61% ~10s
|++++++++++++++++++++++++++++++++ | 62% ~09s
|++++++++++++++++++++++++++++++++ | 64% ~09s
|+++++++++++++++++++++++++++++++++ | 65% ~09s
|+++++++++++++++++++++++++++++++++ | 66% ~09s
|++++++++++++++++++++++++++++++++++ | 67% ~08s
|++++++++++++++++++++++++++++++++++ | 68% ~08s
|+++++++++++++++++++++++++++++++++++ | 69% ~08s
|+++++++++++++++++++++++++++++++++++ | 70% ~08s
|++++++++++++++++++++++++++++++++++++ | 71% ~07s
|++++++++++++++++++++++++++++++++++++ | 72% ~07s
|+++++++++++++++++++++++++++++++++++++ | 73% ~07s
|+++++++++++++++++++++++++++++++++++++ | 74% ~06s
|++++++++++++++++++++++++++++++++++++++ | 75% ~06s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~06s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~06s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~05s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~05s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~05s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~05s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~04s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~04s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~04s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~04s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 25s
| | 0 % ~calculating
|+ | 1 % ~32s
|+ | 2 % ~37s
|++ | 3 % ~37s
|++ | 4 % ~36s
|+++ | 5 % ~36s
|+++ | 6 % ~34s
|++++ | 7 % ~33s
|++++ | 8 % ~33s
|+++++ | 9 % ~33s
|+++++ | 10% ~32s
|++++++ | 11% ~31s
|++++++ | 12% ~31s
|+++++++ | 13% ~31s
|+++++++ | 14% ~30s
|++++++++ | 15% ~30s
|++++++++ | 16% ~30s
|+++++++++ | 17% ~29s
|+++++++++ | 18% ~29s
|++++++++++ | 19% ~28s
|++++++++++ | 20% ~28s
|+++++++++++ | 21% ~27s
|+++++++++++ | 22% ~27s
|++++++++++++ | 23% ~26s
|++++++++++++ | 24% ~26s
|+++++++++++++ | 25% ~26s
|+++++++++++++ | 26% ~26s
|++++++++++++++ | 27% ~25s
|++++++++++++++ | 28% ~25s
|+++++++++++++++ | 29% ~24s
|+++++++++++++++ | 30% ~24s
|++++++++++++++++ | 31% ~24s
|++++++++++++++++ | 32% ~23s
|+++++++++++++++++ | 33% ~23s
|+++++++++++++++++ | 34% ~22s
|++++++++++++++++++ | 35% ~22s
|++++++++++++++++++ | 36% ~22s
|+++++++++++++++++++ | 37% ~21s
|+++++++++++++++++++ | 38% ~21s
|++++++++++++++++++++ | 39% ~20s
|++++++++++++++++++++ | 40% ~20s
|+++++++++++++++++++++ | 41% ~20s
|+++++++++++++++++++++ | 42% ~19s
|++++++++++++++++++++++ | 43% ~19s
|++++++++++++++++++++++ | 44% ~19s
|+++++++++++++++++++++++ | 45% ~18s
|+++++++++++++++++++++++ | 46% ~18s
|++++++++++++++++++++++++ | 47% ~18s
|++++++++++++++++++++++++ | 48% ~17s
|+++++++++++++++++++++++++ | 49% ~17s
|+++++++++++++++++++++++++ | 50% ~16s
|++++++++++++++++++++++++++ | 51% ~16s
|++++++++++++++++++++++++++ | 52% ~16s
|+++++++++++++++++++++++++++ | 53% ~15s
|+++++++++++++++++++++++++++ | 54% ~15s
|++++++++++++++++++++++++++++ | 55% ~15s
|++++++++++++++++++++++++++++ | 56% ~14s
|+++++++++++++++++++++++++++++ | 57% ~14s
|+++++++++++++++++++++++++++++ | 58% ~14s
|++++++++++++++++++++++++++++++ | 59% ~13s
|++++++++++++++++++++++++++++++ | 60% ~13s
|+++++++++++++++++++++++++++++++ | 61% ~13s
|+++++++++++++++++++++++++++++++ | 62% ~12s
|++++++++++++++++++++++++++++++++ | 63% ~12s
|++++++++++++++++++++++++++++++++ | 64% ~12s
|+++++++++++++++++++++++++++++++++ | 65% ~11s
|+++++++++++++++++++++++++++++++++ | 66% ~11s
|++++++++++++++++++++++++++++++++++ | 67% ~11s
|++++++++++++++++++++++++++++++++++ | 68% ~10s
|+++++++++++++++++++++++++++++++++++ | 69% ~10s
|+++++++++++++++++++++++++++++++++++ | 70% ~10s
|++++++++++++++++++++++++++++++++++++ | 71% ~09s
|++++++++++++++++++++++++++++++++++++ | 72% ~09s
|+++++++++++++++++++++++++++++++++++++ | 73% ~09s
|+++++++++++++++++++++++++++++++++++++ | 74% ~08s
|++++++++++++++++++++++++++++++++++++++ | 75% ~08s
|++++++++++++++++++++++++++++++++++++++ | 76% ~08s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~07s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~07s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~07s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~06s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~06s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~06s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~05s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~05s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~05s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~04s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~04s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 32s
| | 0 % ~calculating
|+ | 1 % ~24s
|++ | 2 % ~24s
|++ | 3 % ~25s
|+++ | 4 % ~24s
|+++ | 5 % ~25s
|++++ | 6 % ~25s
|++++ | 7 % ~24s
|+++++ | 8 % ~24s
|+++++ | 9 % ~24s
|++++++ | 10% ~23s
|++++++ | 11% ~23s
|+++++++ | 12% ~23s
|+++++++ | 14% ~23s
|++++++++ | 15% ~24s
|++++++++ | 16% ~23s
|+++++++++ | 17% ~23s
|+++++++++ | 18% ~22s
|++++++++++ | 19% ~22s
|++++++++++ | 20% ~22s
|+++++++++++ | 21% ~21s
|+++++++++++ | 22% ~21s
|++++++++++++ | 23% ~21s
|++++++++++++ | 24% ~20s
|+++++++++++++ | 25% ~20s
|++++++++++++++ | 26% ~20s
|++++++++++++++ | 27% ~19s
|+++++++++++++++ | 28% ~19s
|+++++++++++++++ | 29% ~19s
|++++++++++++++++ | 30% ~19s
|++++++++++++++++ | 31% ~18s
|+++++++++++++++++ | 32% ~18s
|+++++++++++++++++ | 33% ~18s
|++++++++++++++++++ | 34% ~17s
|++++++++++++++++++ | 35% ~17s
|+++++++++++++++++++ | 36% ~17s
|+++++++++++++++++++ | 38% ~16s
|++++++++++++++++++++ | 39% ~16s
|++++++++++++++++++++ | 40% ~16s
|+++++++++++++++++++++ | 41% ~16s
|+++++++++++++++++++++ | 42% ~15s
|++++++++++++++++++++++ | 43% ~15s
|++++++++++++++++++++++ | 44% ~15s
|+++++++++++++++++++++++ | 45% ~14s
|+++++++++++++++++++++++ | 46% ~14s
|++++++++++++++++++++++++ | 47% ~14s
|++++++++++++++++++++++++ | 48% ~14s
|+++++++++++++++++++++++++ | 49% ~13s
|+++++++++++++++++++++++++ | 50% ~13s
|++++++++++++++++++++++++++ | 51% ~13s
|+++++++++++++++++++++++++++ | 52% ~12s
|+++++++++++++++++++++++++++ | 53% ~12s
|++++++++++++++++++++++++++++ | 54% ~12s
|++++++++++++++++++++++++++++ | 55% ~12s
|+++++++++++++++++++++++++++++ | 56% ~11s
|+++++++++++++++++++++++++++++ | 57% ~11s
|++++++++++++++++++++++++++++++ | 58% ~11s
|++++++++++++++++++++++++++++++ | 59% ~11s
|+++++++++++++++++++++++++++++++ | 60% ~10s
|+++++++++++++++++++++++++++++++ | 61% ~10s
|++++++++++++++++++++++++++++++++ | 62% ~10s
|++++++++++++++++++++++++++++++++ | 64% ~09s
|+++++++++++++++++++++++++++++++++ | 65% ~09s
|+++++++++++++++++++++++++++++++++ | 66% ~09s
|++++++++++++++++++++++++++++++++++ | 67% ~09s
|++++++++++++++++++++++++++++++++++ | 68% ~08s
|+++++++++++++++++++++++++++++++++++ | 69% ~08s
|+++++++++++++++++++++++++++++++++++ | 70% ~08s
|++++++++++++++++++++++++++++++++++++ | 71% ~08s
|++++++++++++++++++++++++++++++++++++ | 72% ~07s
|+++++++++++++++++++++++++++++++++++++ | 73% ~07s
|+++++++++++++++++++++++++++++++++++++ | 74% ~07s
|++++++++++++++++++++++++++++++++++++++ | 75% ~06s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~06s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~06s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~06s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~05s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~05s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~05s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~05s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~04s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~04s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~04s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 26s
| | 0 % ~calculating
|+ | 1 % ~19s
|++ | 2 % ~19s
|++ | 3 % ~19s
|+++ | 4 % ~19s
|+++ | 5 % ~19s
|++++ | 6 % ~18s
|++++ | 7 % ~18s
|+++++ | 8 % ~18s
|+++++ | 9 % ~17s
|++++++ | 10% ~17s
|++++++ | 11% ~17s
|+++++++ | 12% ~17s
|+++++++ | 13% ~16s
|++++++++ | 14% ~16s
|++++++++ | 15% ~16s
|+++++++++ | 16% ~16s
|+++++++++ | 17% ~16s
|++++++++++ | 18% ~15s
|++++++++++ | 19% ~15s
|+++++++++++ | 20% ~15s
|+++++++++++ | 21% ~15s
|++++++++++++ | 22% ~15s
|++++++++++++ | 23% ~15s
|+++++++++++++ | 24% ~15s
|+++++++++++++ | 26% ~15s
|++++++++++++++ | 27% ~15s
|++++++++++++++ | 28% ~14s
|+++++++++++++++ | 29% ~14s
|+++++++++++++++ | 30% ~14s
|++++++++++++++++ | 31% ~14s
|++++++++++++++++ | 32% ~13s
|+++++++++++++++++ | 33% ~13s
|+++++++++++++++++ | 34% ~13s
|++++++++++++++++++ | 35% ~13s
|++++++++++++++++++ | 36% ~13s
|+++++++++++++++++++ | 37% ~12s
|+++++++++++++++++++ | 38% ~12s
|++++++++++++++++++++ | 39% ~12s
|++++++++++++++++++++ | 40% ~12s
|+++++++++++++++++++++ | 41% ~11s
|+++++++++++++++++++++ | 42% ~11s
|++++++++++++++++++++++ | 43% ~11s
|++++++++++++++++++++++ | 44% ~11s
|+++++++++++++++++++++++ | 45% ~11s
|+++++++++++++++++++++++ | 46% ~10s
|++++++++++++++++++++++++ | 47% ~10s
|++++++++++++++++++++++++ | 48% ~10s
|+++++++++++++++++++++++++ | 49% ~10s
|+++++++++++++++++++++++++ | 50% ~10s
|++++++++++++++++++++++++++ | 51% ~09s
|+++++++++++++++++++++++++++ | 52% ~09s
|+++++++++++++++++++++++++++ | 53% ~09s
|++++++++++++++++++++++++++++ | 54% ~09s
|++++++++++++++++++++++++++++ | 55% ~09s
|+++++++++++++++++++++++++++++ | 56% ~08s
|+++++++++++++++++++++++++++++ | 57% ~08s
|++++++++++++++++++++++++++++++ | 58% ~08s
|++++++++++++++++++++++++++++++ | 59% ~08s
|+++++++++++++++++++++++++++++++ | 60% ~08s
|+++++++++++++++++++++++++++++++ | 61% ~07s
|++++++++++++++++++++++++++++++++ | 62% ~07s
|++++++++++++++++++++++++++++++++ | 63% ~07s
|+++++++++++++++++++++++++++++++++ | 64% ~07s
|+++++++++++++++++++++++++++++++++ | 65% ~07s
|++++++++++++++++++++++++++++++++++ | 66% ~06s
|++++++++++++++++++++++++++++++++++ | 67% ~06s
|+++++++++++++++++++++++++++++++++++ | 68% ~06s
|+++++++++++++++++++++++++++++++++++ | 69% ~06s
|++++++++++++++++++++++++++++++++++++ | 70% ~06s
|++++++++++++++++++++++++++++++++++++ | 71% ~05s
|+++++++++++++++++++++++++++++++++++++ | 72% ~05s
|+++++++++++++++++++++++++++++++++++++ | 73% ~05s
|++++++++++++++++++++++++++++++++++++++ | 74% ~05s
|++++++++++++++++++++++++++++++++++++++ | 76% ~05s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~04s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~04s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~04s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 19s
| | 0 % ~calculating
|+ | 1 % ~19s
|++ | 2 % ~19s
|++ | 3 % ~19s
|+++ | 4 % ~19s
|+++ | 5 % ~19s
|++++ | 6 % ~19s
|++++ | 7 % ~19s
|+++++ | 8 % ~21s
|+++++ | 9 % ~21s
|++++++ | 11% ~20s
|++++++ | 12% ~20s
|+++++++ | 13% ~19s
|+++++++ | 14% ~19s
|++++++++ | 15% ~19s
|++++++++ | 16% ~18s
|+++++++++ | 17% ~18s
|+++++++++ | 18% ~18s
|++++++++++ | 19% ~17s
|++++++++++ | 20% ~17s
|+++++++++++ | 21% ~17s
|++++++++++++ | 22% ~17s
|++++++++++++ | 23% ~16s
|+++++++++++++ | 24% ~16s
|+++++++++++++ | 25% ~16s
|++++++++++++++ | 26% ~16s
|++++++++++++++ | 27% ~15s
|+++++++++++++++ | 28% ~15s
|+++++++++++++++ | 29% ~15s
|++++++++++++++++ | 31% ~15s
|++++++++++++++++ | 32% ~14s
|+++++++++++++++++ | 33% ~14s
|+++++++++++++++++ | 34% ~14s
|++++++++++++++++++ | 35% ~14s
|++++++++++++++++++ | 36% ~14s
|+++++++++++++++++++ | 37% ~13s
|+++++++++++++++++++ | 38% ~13s
|++++++++++++++++++++ | 39% ~13s
|++++++++++++++++++++ | 40% ~13s
|+++++++++++++++++++++ | 41% ~12s
|++++++++++++++++++++++ | 42% ~12s
|++++++++++++++++++++++ | 43% ~12s
|+++++++++++++++++++++++ | 44% ~12s
|+++++++++++++++++++++++ | 45% ~12s
|++++++++++++++++++++++++ | 46% ~11s
|++++++++++++++++++++++++ | 47% ~11s
|+++++++++++++++++++++++++ | 48% ~11s
|+++++++++++++++++++++++++ | 49% ~11s
|++++++++++++++++++++++++++ | 51% ~10s
|++++++++++++++++++++++++++ | 52% ~10s
|+++++++++++++++++++++++++++ | 53% ~10s
|+++++++++++++++++++++++++++ | 54% ~10s
|++++++++++++++++++++++++++++ | 55% ~10s
|++++++++++++++++++++++++++++ | 56% ~10s
|+++++++++++++++++++++++++++++ | 57% ~09s
|+++++++++++++++++++++++++++++ | 58% ~09s
|++++++++++++++++++++++++++++++ | 59% ~09s
|++++++++++++++++++++++++++++++ | 60% ~09s
|+++++++++++++++++++++++++++++++ | 61% ~08s
|++++++++++++++++++++++++++++++++ | 62% ~08s
|++++++++++++++++++++++++++++++++ | 63% ~08s
|+++++++++++++++++++++++++++++++++ | 64% ~08s
|+++++++++++++++++++++++++++++++++ | 65% ~07s
|++++++++++++++++++++++++++++++++++ | 66% ~07s
|++++++++++++++++++++++++++++++++++ | 67% ~07s
|+++++++++++++++++++++++++++++++++++ | 68% ~07s
|+++++++++++++++++++++++++++++++++++ | 69% ~06s
|++++++++++++++++++++++++++++++++++++ | 71% ~06s
|++++++++++++++++++++++++++++++++++++ | 72% ~06s
|+++++++++++++++++++++++++++++++++++++ | 73% ~06s
|+++++++++++++++++++++++++++++++++++++ | 74% ~06s
|++++++++++++++++++++++++++++++++++++++ | 75% ~05s
|++++++++++++++++++++++++++++++++++++++ | 76% ~05s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~05s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~05s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~04s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~04s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~04s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~04s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 21s
| | 0 % ~calculating
|+ | 1 % ~44s
|++ | 2 % ~42s
|++ | 3 % ~41s
|+++ | 4 % ~38s
|+++ | 5 % ~38s
|++++ | 6 % ~37s
|++++ | 7 % ~37s
|+++++ | 8 % ~37s
|+++++ | 9 % ~36s
|++++++ | 10% ~36s
|++++++ | 11% ~35s
|+++++++ | 12% ~35s
|+++++++ | 13% ~34s
|++++++++ | 14% ~35s
|++++++++ | 15% ~35s
|+++++++++ | 16% ~35s
|+++++++++ | 17% ~34s
|++++++++++ | 18% ~34s
|++++++++++ | 19% ~33s
|+++++++++++ | 20% ~33s
|+++++++++++ | 21% ~32s
|++++++++++++ | 22% ~32s
|++++++++++++ | 23% ~31s
|+++++++++++++ | 24% ~31s
|+++++++++++++ | 25% ~30s
|++++++++++++++ | 26% ~30s
|++++++++++++++ | 27% ~30s
|+++++++++++++++ | 28% ~29s
|+++++++++++++++ | 29% ~29s
|++++++++++++++++ | 30% ~28s
|++++++++++++++++ | 31% ~28s
|+++++++++++++++++ | 32% ~27s
|+++++++++++++++++ | 33% ~27s
|++++++++++++++++++ | 34% ~26s
|++++++++++++++++++ | 35% ~26s
|+++++++++++++++++++ | 36% ~26s
|+++++++++++++++++++ | 37% ~25s
|++++++++++++++++++++ | 38% ~25s
|++++++++++++++++++++ | 39% ~25s
|+++++++++++++++++++++ | 40% ~24s
|+++++++++++++++++++++ | 41% ~24s
|++++++++++++++++++++++ | 42% ~23s
|++++++++++++++++++++++ | 43% ~23s
|+++++++++++++++++++++++ | 44% ~22s
|+++++++++++++++++++++++ | 45% ~22s
|++++++++++++++++++++++++ | 46% ~22s
|++++++++++++++++++++++++ | 47% ~21s
|+++++++++++++++++++++++++ | 48% ~21s
|+++++++++++++++++++++++++ | 49% ~20s
|++++++++++++++++++++++++++ | 51% ~20s
|++++++++++++++++++++++++++ | 52% ~19s
|+++++++++++++++++++++++++++ | 53% ~19s
|+++++++++++++++++++++++++++ | 54% ~19s
|++++++++++++++++++++++++++++ | 55% ~18s
|++++++++++++++++++++++++++++ | 56% ~18s
|+++++++++++++++++++++++++++++ | 57% ~17s
|+++++++++++++++++++++++++++++ | 58% ~17s
|++++++++++++++++++++++++++++++ | 59% ~16s
|++++++++++++++++++++++++++++++ | 60% ~16s
|+++++++++++++++++++++++++++++++ | 61% ~16s
|+++++++++++++++++++++++++++++++ | 62% ~15s
|++++++++++++++++++++++++++++++++ | 63% ~15s
|++++++++++++++++++++++++++++++++ | 64% ~15s
|+++++++++++++++++++++++++++++++++ | 65% ~14s
|+++++++++++++++++++++++++++++++++ | 66% ~14s
|++++++++++++++++++++++++++++++++++ | 67% ~13s
|++++++++++++++++++++++++++++++++++ | 68% ~13s
|+++++++++++++++++++++++++++++++++++ | 69% ~13s
|+++++++++++++++++++++++++++++++++++ | 70% ~12s
|++++++++++++++++++++++++++++++++++++ | 71% ~12s
|++++++++++++++++++++++++++++++++++++ | 72% ~11s
|+++++++++++++++++++++++++++++++++++++ | 73% ~11s
|+++++++++++++++++++++++++++++++++++++ | 74% ~11s
|++++++++++++++++++++++++++++++++++++++ | 75% ~10s
|++++++++++++++++++++++++++++++++++++++ | 76% ~10s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~09s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~09s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~08s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~08s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~08s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~07s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~07s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~06s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~06s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~06s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~05s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 40s
| | 0 % ~calculating
|+ | 1 % ~57s
|++ | 2 % ~45s
|++ | 3 % ~40s
|+++ | 4 % ~37s
|+++ | 5 % ~35s
|++++ | 6 % ~34s
|++++ | 7 % ~33s
|+++++ | 8 % ~32s
|+++++ | 9 % ~31s
|++++++ | 10% ~31s
|++++++ | 11% ~30s
|+++++++ | 12% ~30s
|+++++++ | 13% ~29s
|++++++++ | 14% ~29s
|++++++++ | 15% ~29s
|+++++++++ | 16% ~28s
|+++++++++ | 17% ~28s
|++++++++++ | 18% ~27s
|++++++++++ | 19% ~27s
|+++++++++++ | 20% ~26s
|+++++++++++ | 21% ~26s
|++++++++++++ | 22% ~26s
|++++++++++++ | 23% ~25s
|+++++++++++++ | 24% ~25s
|+++++++++++++ | 26% ~24s
|++++++++++++++ | 27% ~24s
|++++++++++++++ | 28% ~24s
|+++++++++++++++ | 29% ~23s
|+++++++++++++++ | 30% ~23s
|++++++++++++++++ | 31% ~23s
|++++++++++++++++ | 32% ~23s
|+++++++++++++++++ | 33% ~22s
|+++++++++++++++++ | 34% ~22s
|++++++++++++++++++ | 35% ~21s
|++++++++++++++++++ | 36% ~21s
|+++++++++++++++++++ | 37% ~21s
|+++++++++++++++++++ | 38% ~20s
|++++++++++++++++++++ | 39% ~20s
|++++++++++++++++++++ | 40% ~20s
|+++++++++++++++++++++ | 41% ~19s
|+++++++++++++++++++++ | 42% ~19s
|++++++++++++++++++++++ | 43% ~19s
|++++++++++++++++++++++ | 44% ~18s
|+++++++++++++++++++++++ | 45% ~18s
|+++++++++++++++++++++++ | 46% ~18s
|++++++++++++++++++++++++ | 47% ~17s
|++++++++++++++++++++++++ | 48% ~17s
|+++++++++++++++++++++++++ | 49% ~17s
|+++++++++++++++++++++++++ | 50% ~16s
|++++++++++++++++++++++++++ | 51% ~16s
|+++++++++++++++++++++++++++ | 52% ~16s
|+++++++++++++++++++++++++++ | 53% ~15s
|++++++++++++++++++++++++++++ | 54% ~15s
|++++++++++++++++++++++++++++ | 55% ~15s
|+++++++++++++++++++++++++++++ | 56% ~14s
|+++++++++++++++++++++++++++++ | 57% ~14s
|++++++++++++++++++++++++++++++ | 58% ~14s
|++++++++++++++++++++++++++++++ | 59% ~14s
|+++++++++++++++++++++++++++++++ | 60% ~13s
|+++++++++++++++++++++++++++++++ | 61% ~13s
|++++++++++++++++++++++++++++++++ | 62% ~12s
|++++++++++++++++++++++++++++++++ | 63% ~12s
|+++++++++++++++++++++++++++++++++ | 64% ~12s
|+++++++++++++++++++++++++++++++++ | 65% ~11s
|++++++++++++++++++++++++++++++++++ | 66% ~11s
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 33sDisplay the top markers you computed above.
tiss.markers %>% group_by(cluster) %>% top_n(10, avg_logFC)Assigning cell type identity to clusters
At a coarse level, we can use canonical markers to match the unbiased clustering to known cell types:
tiss <- StashIdent(object = tiss, save.name = "cluster.ids")
cluster.ids <- c(0, 1, 2, 3, 4, 5, 6, 7, 8, 9)
cell_ontology_class <- c(
"endothelial cell",
'kidney collecting duct intercalated cell',
'epithelial cell of proximal tubule',
"kidney collecting duct principal cell",
"epithelial cell of proximal tubule",
'endothelial cell',
"kidney proximal straight tubule epithelial cell",
"kidney proximal straight tubule epithelial cell",
"macrophage",
"natural killer cell")
free_annotation <- rep(NA, max(as.numeric(tiss@ident)))
validate_cell_ontology(cell_ontology_class)
cell_ontology_id = convert_to_cell_ontology_id(cell_ontology_class)
tiss@meta.data['free_annotation'] <- as.character(plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = free_annotation))
tiss@meta.data['cell_ontology_class'] <- as.character(plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = cell_ontology_class))
tiss@meta.data['cell_ontology_id'] <- as.character(plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = cell_ontology_id))Checking for batch effects
Color by metadata, like plate barcode, to check for batch effects.
TSNEPlot(object = tiss, do.return = TRUE, group.by = "plate.barcode")
Print a table showing the count of cells in each identity category from each plate.
table(as.character(tiss@ident), as.character(tiss@meta.data$plate.barcode))
B001717 B002775 MAA000545 MAA000752 MAA000801 MAA000922
0 5 11 16 7 23 9
1 20 17 1 3 21 3
2 10 7 12 6 20 5
3 5 8 9 14 18 4
4 3 3 13 15 18 5
5 40 7 3 2 1 2
6 23 5 1 4 18 2
7 3 8 11 6 10 7
8 8 2 1 8 17 3
9 6 3 2 0 3 0Subset and iterate
We can repeat the above analysis on a subset of cells, defined using cluster IDs or some other metadata. This is a good way to drill down and find substructure.
First subset
# Subset data based on cluster id
subtiss <- SubsetData(object = tiss, ident.use = c(6,4,9,2), do.center = F, do.scale = F, cells.use = )
# To subset data based on cell_ontology_class or other metadata, you can explicitly pass cell names
# anno = 'exocrine cells'
# cells.to.use = tiss@cell.names[which(tiss@meta.data$cell_ontology_class == anno)]
# subtiss <- SubsetData(object = tiss, cells.use = cells.to.use, do.center = F, do.scale = F)subtiss <- NormalizeData(object = subtiss)Performing log-normalization
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|subtiss <- ScaleData(object = subtiss, vars.to.regress = c("nReads", "percent.ribo","Rn45s"))[1] "Regressing out nReads" "Regressing out percent.ribo" "Regressing out Rn45s"
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[1] "Scaling data matrix"
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|===========================================================================================================================| 100%subtiss <- FindVariableGenes(object = subtiss, do.plot = TRUE, x.high.cutoff = Inf, y.cutoff = 0.8)Calculating gene means
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
subtiss <- RunPCA(object = subtiss, pcs.compute = 20)[1] "PC1"
[1] "Tcf7" "Frat1" "Pik3ip1" "Gatm" "Ankrd24" "Zfand2a" "Il7r" "Adcy3"
[9] "Cd63" "4632428N05Rik" "Inpp1" "Zfc3h1" "Tnrc6a" "Oxct1" "Yipf2" "Ermp1"
[17] "Dnajc18" "Spp1" "Hao2" "Eid1" "Msl3" "Slc5a3" "Fam49a" "Cep97"
[25] "L3mbtl3" "Gm6225" "Pms1" "Ankrd16" "Fam160b1" "Gnl3"
[1] ""
[1] "Nkg7" "AW112010" "Anxa2" "Arhgdib" "Selplg" "Tmsb4x" "Il2rb" "Coro1a" "B4galnt1" "H2-Q6" "Psmb8"
[12] "Sla" "Gata3" "Esyt1" "Hmha1" "Rac2" "Gem" "Itgb2" "Ccl5" "Anxa6" "Ifngr1" "Gimap3"
[23] "Laptm5" "Cd48" "Fgl2" "Cd7" "Vim" "Fxyd5" "Tspan32" "Lsp1"
[1] ""
[1] ""
[1] "PC2"
[1] "Cd79a" "Cd79b" "Cd74" "Ly6d" "Ms4a1" "H2-Aa" "Prkcb" "H2-Eb1" "Cnp" "Hvcn1" "Ltb" "Faim3"
[13] "H2-Ab1" "Fli1" "Cd72" "Spib" "H2-DMb2" "Cd69" "Ccr7" "Mef2c" "Cd22" "Filip1l" "Ebf1" "Lrmp"
[25] "Cd83" "Dok3" "Pik3c2b" "Rnase6" "Vpreb3" "H2-Ob"
[1] ""
[1] "Ccnd2" "Fxyd5" "Lat" "Vps37b" "Cd28" "Itgb7" "Lck" "Tnfaip3"
[9] "Cd3g" "Lcp2" "Cd3d" "Vav1" "Frat1" "Tcf7" "Ms4a4b" "AW112010"
[17] "A630001G21Rik" "Emb" "Cd8b1" "Zap70" "Itgb2" "Ms4a6b" "Bcl2" "Nkg7"
[25] "Ctsw" "Ccl5" "Ctsc" "Anxa2" "Gimap3" "S1pr1"
[1] ""
[1] ""
[1] "PC3"
[1] "Unc93b1" "Crip1" "Gramd3" "Pim2" "Ralgps2" "Tmem39b" "Atic" "Tgif1"
[9] "Kdm6b" "Hsf2" "Papd4" "Lat2" "Btbd7" "Fcrl1" "Fen1" "Spib"
[17] "Socs5" "Utp18" "Scmh1" "Zbtb2" "Zfp296" "Ankrd37" "1110031I02Rik" "Dffb"
[25] "Rasgrp1" "Adck2" "Ccnj" "Usp42" "Zc3h10" "B4galt7"
[1] ""
[1] "Grap" "Rhof" "Elmo1" "Rhoh" "Lpar6" "H2-T24" "Faim3" "Sept6"
[9] "Cd83" "Tmem209" "H2-DMa" "Rfc4" "Ly9" "Sell" "Gimap7" "Ep300"
[17] "Shisa5" "Hltf" "Slain1" "Rftn1" "Akna" "Ppp1r12c" "Mical1" "Mif4gd"
[25] "Ucp2" "Zfp263" "A830007P12Rik" "Asb6" "Mapkapk3" "Pcif1"
[1] ""
[1] ""
[1] "PC4"
[1] "Sell" "Cxcr5" "Ctss" "Slfn2" "Fcer2a" "Blk" "Ep300" "Cmtm7" "Mef2c" "Gimap7"
[11] "Serpinb1a" "H2-Ob" "Ly9" "Prkd2" "Tmem209" "Rnf20" "Cd69" "Gm14446" "Cd55" "Eral1"
[21] "Akna" "Cd72" "Thap11" "Fam53b" "Myst3" "Dok3" "Nrm" "Pml" "Usp25" "Bank1"
[1] ""
[1] "Ptpn4" "2810013P06Rik" "Gpr171" "Pcif1" "Rcsd1" "Arid4b" "Hltf" "Gm6225"
[9] "Rgs19" "Asb6" "A830007P12Rik" "Filip1l" "Rasal3" "Nipsnap3b" "R3hcc1" "Zfp263"
[17] "Cybb" "Gstm4" "Rftn1" "Ppp1r12c" "Rassf4" "Btbd10" "Rab3gap1" "Abcg1"
[25] "Pcnt" "Pik3c2b" "Pms1" "Ngdn" "Klre1" "Serpinb6b"
[1] ""
[1] ""
[1] "PC5"
[1] "Fcho1" "Cd5" "Tacc1" "Cd247" "Pwwp2a" "Smc4" "Nufip2" "Orc3" "Rapgef6"
[10] "Zc3h12d" "Esyt1" "Klhdc4" "Wars2" "Rc3h1" "Tab3" "Tmem161a" "Galnt6" "Ube2g2"
[19] "Mical1" "Cdkn2aipnl" "Zfp954" "Ddx10" "Mapkapk3" "Rab28" "Ccr2" "Rhof" "Cd4"
[28] "Prpf3" "Faf1" "Bzw2"
[1] ""
[1] "Pkm2" "P2ry10" "Arhgap30" "Lcp2" "Zap70" "Xpo1" "Cd8b1" "Lck" "Trim30a" "Wdr3"
[11] "Lrrfip1" "Ms4a4b" "Ier5" "Smek1" "Plekha5" "Nrm" "Mef2c" "Sh3kbp1" "Cxcr5" "Serpina3g"
[21] "Fcer2a" "Stat1" "Serpinb1a" "Casp8ap2" "Mad2l1bp" "Fyn" "Esco1" "Ctsw" "Slc35e1" "Trp53inp1"
[1] ""
[1] ""subtiss <- ProjectPCA(object = subtiss, do.print = FALSE)Run Principal Component Analysis.
subtiss <- RunPCA(object = subtiss, do.print = FALSE)
subtiss <- ProjectPCA(object = subtiss, do.print = FALSE)# If this fails for your subset, it may be that cells.use is more cells than you have left! Try reducing it.
PCHeatmap(object = subtiss, pc.use = 1:3, cells.use = 290, do.balanced = TRUE, label.columns = FALSE, num.genes = 3)
Later on (in FindClusters and TSNE) you will pick a number of principal components to use. This has the effect of keeping the major directions of variation in the data and, ideally, supressing noise. There is no correct answer to the number to use, but a decent rule of thumb is to go until the plot plateaus.
PCElbowPlot(object = subtiss)
Choose the number of principal components to use.
# Set number of principal components.
sub.n.pcs = 10The clustering is performed based on a nearest neighbors graph. Cells that have similar expression will be joined together. The Louvain algorithm looks for groups of cells with high modularity–more connections within the group than between groups. The resolution parameter determines the scale…higher resolution will give more clusters, lower resolution will give fewer.
# Set resolution
sub.res.used <- 1
subtiss <- FindClusters(object = subtiss, reduction.type = "pca", dims.use = 1:sub.n.pcs,
resolution = sub.res.used, print.output = 0, save.SNN = TRUE)To visualize
# If cells are too spread out, you can raise the perplexity. If you have few cells, try a lower perplexity (but never less than 10).
subtiss <- RunTSNE(object = subtiss, dims.use = 1:sub.n.pcs, seed.use = 10, perplexity=10)# note that you can set do.label=T to help label individual clusters
TSNEPlot(object = subtiss, do.label = T)
subtiss.markers <- FindAllMarkers(object = subtiss, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 04s
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 07s
| | 0 % ~calculating
|+ | 1 % ~09s
|++ | 2 % ~09s
|++ | 3 % ~08s
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|++++ | 6 % ~08s
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|++++++++ | 15% ~07s
|++++++++ | 16% ~07s
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|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 08s
| | 0 % ~calculating
|+ | 1 % ~06s
|+ | 2 % ~06s
|++ | 3 % ~06s
|++ | 4 % ~06s
|+++ | 5 % ~06s
|+++ | 6 % ~06s
|++++ | 7 % ~06s
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|++++++++ | 15% ~05s
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 06ssubtiss.markers %>% group_by(cluster) %>% top_n(3)Selecting by geneCheck expression of genes of interset.
genes_to_check = c('Lgr5','Fabp5','Marcks','Zfp830','Slc6a8')
FeaturePlot(subtiss, genes_to_check, pt.size = 1)
Dotplots let you see the intensity of exppression and the fraction of cells expressing for each of your genes of interest.
# To change the y-axis to show raw counts, add use.raw = T.
DotPlot(subtiss, genes_to_check, plot.legend = T)
How big are the clusters?
table(subtiss@ident)
0 1 2 3
62 47 39 36 Checking for batch effects
Color by metadata, like plate barcode, to check for batch effects.
TSNEPlot(object = subtiss, do.return = TRUE, group.by = "plate.barcode")
Print a table showing the count of cells in each identity category from each plate.
table(as.character(subtiss@ident), as.character(subtiss@meta.data$plate.barcode))
B001717 B002775 MAA000545 MAA000752 MAA000801 MAA000922
0 8 11 7 12 19 5
1 1 1 16 8 16 5
2 6 2 5 4 21 1
3 27 4 0 1 3 1Save the Robject for later
When you save the annotated tissue, please give it a name.
filename = here('00_data_ingest', '04_tissue_robj_generated',
paste0("facs", tissue_of_interest, "_seurat_tiss.Robj"))
print(filename)[1] "/Users/olgabot/code/tabula-muris/00_data_ingest/04_tissue_robj_generated/facsKidney_seurat_tiss.Robj"save(tiss, file=filename)# To reload a saved object
# filename = here('00_data_ingest', '04_tissue_robj_generated',
# paste0("facs", tissue_of_interest, "_seurat_tiss.Robj"))
# load(file=filename)Export the final metadata
So that Biohub can easily combine all your cell_ontology_classs, please export them as a simple csv.
head(tiss@meta.data)filename = here('00_data_ingest', '03_tissue_cell_ontology_class_csv',
paste0(tissue_of_interest, "_cell_ontology_class.csv"))
write.csv(tiss@meta.data[,c('plate.barcode','cell_ontology_class','cell_ontology_id')], file=filename)cannot open file '/Users/olgabot/code/tabula-muris/00_data_ingest/03_tissue_cell_ontology_class_csv/Kidney_cell_ontology_class.csv': No such file or directoryError in file(file, ifelse(append, "a", "w")) :
cannot open the 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